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resume_builder/support/achievement_reframing_guide.md

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+# Achievement Reframing Guide — Dennis Thiessen
+
+> Generated: 2026-03-28
+> Role types from config.md: Staff/Senior Data Engineer | Analytics Engineer | ML/AI Engineer | Data Platform/Infra
+
+---
+
+## How to Use
+
+Each achievement has a **Significance** line (why it matters to any reader) and a role-type table showing how to frame, which verb to lead with, and whether to include or omit for that audience. Use this guide when selecting and ordering bullets during resume generation.
+
+**Priority tiers:**
+- **HIGH** — Lead bullet or include in all variants of this position
+- **MED** — Include if page budget allows; adjust framing per role type
+- **LOW** — Omit from resume; include in full CV or CL only
+
+---
+
+## SWISSCOM ACHIEVEMENTS
+
+---
+
+### SW-1: AWS Migration of Legacy ETL Stack
+**Significance:** Demonstrates hands-on cloud migration ownership at scale — a tier-1 signal for all data engineering and platform roles. AWS is the market-dominant cloud; owning a full migration from legacy to serverless is a top-of-market achievement.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | HIGH | Migrated | Lead with scale + operational impact (reduced overhead) |
+| Analytics Engineer | HIGH | Migrated | Lead with "enabling analytics outcomes" — tie to downstream stakeholder value |
+| ML/AI Engineer | MED | Migrated | Frame as "building the data infrastructure enabling ML workflows" |
+| Data Platform/Infra | HIGH | Architected | Lead with cloud-native architecture decisions; de-emphasize analytics framing |
+
+**Overclaiming warning:** No specific throughput/volume numbers available — do not invent. Use qualitative impact (operational overhead reduction, scalability improvement).
+
+---
+
+### SW-2: Component Ownership — Fulfillment ETL Pipelines
+**Significance:** Component Owner is a staff-level accountability signal — owning reliability, compliance, and on-call for business-critical data. Demonstrates senior engineer maturity beyond pure development.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | HIGH | Owned | Lead with accountability: Component Owner, SLA, on-call. This is the flagship bullet. |
+| Analytics Engineer | HIGH | Owned | Frame as "ensuring data availability for downstream analytics" — business impact angle |
+| ML/AI Engineer | MED | Owned | Frame as "reliable data feed for ML model inputs" |
+| Data Platform/Infra | HIGH | Owned | Lead with Kafka/Teradata infrastructure; de-emphasize "Fulfillment domain" context |
+
+**Overclaiming warning:** None — employer-confirmed via Zeugnis.
+
+---
+
+### SW-3: Python Applications on Kubernetes + GitLab CI/CD
+**Significance:** Kubernetes ownership at Staff level in a production environment — paired with GitLab CI/CD — is a strong infrastructure signal. Confirms the "SWE + Ops" hybrid identity from LinkedIn summary.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | HIGH | Deployed | Show DevOps ownership as part of the data engineering role |
+| Analytics Engineer | MED | Deployed | Include only if JD mentions platform ownership; otherwise de-emphasize |
+| ML/AI Engineer | HIGH | Deployed | Frame as "containerized ML-ready Python services on Kubernetes" |
+| Data Platform/Infra | HIGH | Built & operated | Lead with infrastructure automation; K8s + CI/CD is the core signal |
+
+---
+
+### SW-4: B2B Data Products, Stakeholder Analytics & Automation
+**Significance:** Demonstrates the bridge between engineering and business — delivering actionable data to stakeholders while automating operations. Key for Analytics Engineer positioning; supporting for others.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | MED | Delivered | Supporting bullet — shows stakeholder-facing breadth; pair with SW-2 |
+| Analytics Engineer | HIGH | Delivered | LEAD bullet for this role type — emphasize B2B stakeholder impact and dashboard delivery |
+| ML/AI Engineer | LOW | — | Omit or condense; not core ML signal |
+| Data Platform/Infra | LOW | — | Omit; not infrastructure-focused |
+
+---
+
+### SW-5: Security Champion — 3 Consecutive Years
+**Significance:** 3 consecutive years = institutional trust, not just a one-time training. Signals security ownership across the DevSecOps lifecycle — rare for a data engineer to hold this level of security designation.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | MED | Designated | Include as breadth signal for senior roles; shows accountability beyond code |
+| Analytics Engineer | LOW | — | Omit — not differentiating for this audience |
+| ML/AI Engineer | MED | Designated | Include for AI product companies where model security/compliance is relevant |
+| Data Platform/Infra | HIGH | Designated | Lead DevSecOps angle — infrastructure roles care about security compliance |
+
+---
+
+### SW-6: PySpark Backend Engineering
+**Significance:** Confirms Big Data / distributed processing capability at Staff level. Differentiates from Python-only data engineers when JD requires Spark.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| All role types | MED | Applied | Roll into skills section unless JD explicitly requires PySpark — then elevate to bullet |
+| Data Platform/Infra | MED | Applied | Include as distributed processing signal |
+
+---
+
+## BOSCH ACHIEVEMENTS
+
+---
+
+### BS-1: ML Inference Containerization in 24/7 Production (Defect Management Domain)
+**Significance:** Deploying ML models into a continuous, uninterruptible semiconductor production line is a uniquely high-stakes MLOps achievement — far beyond typical "model trained in notebook" experience. The defect management domain (image-based wafer defect classification) adds semiconductor industry specificity — a rare combination of MLOps depth + semiconductor domain expertise.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | HIGH | Containerized | Frame as data pipeline + ML integration — "production ML as part of data infrastructure" |
+| Analytics Engineer | MED | Containerized | For semi industry JDs: include with defect management domain framing — "automated defect classification analytics" |
+| ML/AI Engineer | HIGH | Containerized | FLAGSHIP bullet — lead with "24/7 production ML", automated inference, K8s orchestration, defect detection |
+| Data Platform/Infra | HIGH | Containerized | Lead with Docker/K8s/Ansible infrastructure; de-emphasize ML domain |
+| **Semiconductor JDs** | HIGH | Containerized | Lead with defect management domain — "automated image-based defect classification for 300mm fab"; this is the differentiating signal for semi industry applications |
+
+**Overclaiming warning:** "Significantly reducing manual workload" is the claim — employer Zeugnis says "enabling fully automated image classification". Safe to use. No percentage available — do not invent.
+
+---
+
+### BS-2: Data Services Over OracleDB and Hadoop/ImpalaSQL
+**Significance:** Multi-language (Python/Java/C#) data service development over enterprise-grade databases in a high-throughput manufacturing environment confirms broad data engineering depth and platform-agnostic capability. For semiconductor JDs: these data services fed Defect Management, Parameter Testing, and Process Analysis teams directly.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | HIGH | Built | Lead with data service breadth; multiple languages + Oracle + Hadoop = enterprise DE depth |
+| Analytics Engineer | MED | Built | Frame as "supplying analysis teams with structured data access" |
+| Analytics Engineer (semi JD) | HIGH | Built | Lead with domain: "supplying defect management and parameter testing teams with on-demand data and insights" |
+| ML/AI Engineer | MED | Built | Frame as "data layer enabling ML model inputs" |
+| Data Platform/Infra | HIGH | Built | Lead with Oracle + Hadoop infrastructure combination |
+
+---
+
+### BS-3: Application Owner — Analytics Platforms
+**Significance:** Application Owner is a well-understood seniority signal in German/Swiss tech companies — it means owning the system's lifecycle, not just writing code. SLO definition + training + stakeholder management = staff-level maturity.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | HIGH | Owned | ALWAYS include — clearest seniority signal from Bosch period |
+| Analytics Engineer | HIGH | Owned | Frame as "enabling reliable data access for analysis teams" |
+| ML/AI Engineer | MED | Owned | Include as operational ownership signal |
+| Data Platform/Infra | HIGH | Owned | Frame around SLA + platform reliability angle |
+
+---
+
+### BS-4: ELK Stack PoC — Anomaly Detection & Monitoring
+**Significance:** Self-initiated observability work beyond the core job scope — demonstrates initiative and infrastructure curiosity. ELK + Kafka + Grafana/Prometheus is a recognizable modern observability stack.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | MED | Delivered | Supporting bullet — shows platform breadth; include if space allows |
+| Analytics Engineer | LOW | — | Omit |
+| ML/AI Engineer | MED | Delivered | Frame as anomaly detection ML application |
+| Data Platform/Infra | HIGH | Delivered | Lead observability stack angle — ELK + Prometheus + Grafana |
+
+**Note:** CV-2 only. Include when 2-page resume has budget; always in 5-page CV.
+
+---
+
+### BS-5: Tibco Spotfire C# Extensions
+**Significance:** Minor — niche BI tooling signal. Only relevant if JD specifically mentions Spotfire or C#-based analytics tooling.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| All role types | LOW | — | Omit from resume; include in skills taxonomy only |
+| Analytics Engineer | LOW | Developed | Include only if JD explicitly names Spotfire |
+
+---
+
+## FRAUNHOFER ACHIEVEMENTS
+
+---
+
+### FC-1: SCEDAS Development + CI/CD Pipeline Introduction
+**Significance:** Independently introduced CI/CD to a research team (no prior automation existed) — strong initiative signal. SCEDAS development confirms C# / .NET / SQL depth. The CI/CD angle is more valuable for target roles than the DSS domain.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | MED | Established (for CI/CD) | Lead with CI/CD independence; SCEDAS is context |
+| Analytics Engineer | LOW | — | Omit |
+| ML/AI Engineer | LOW | — | Omit |
+| Data Platform/Infra | MED | Established | Lead with pipeline automation initiative |
+
+---
+
+### FC-2: ARTUS — ML/NLP for Sea Rescue Transcription
+**Significance:** Applied ML/NLP in a safety-critical domain as part of a named research project at a leading European applied research institute. Confirms early ML/NLP exposure (pre-Bosch) — establishes ML thread across career.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | MED | Contributed | Supporting signal — shows ML breadth from earlier career |
+| Analytics Engineer | LOW | — | Omit |
+| ML/AI Engineer | HIGH | Contributed | Include — establishes NLP / ML research background; pair with Bosch ML deployment |
+| Data Platform/Infra | LOW | — | Omit |
+
+**Verb:** ALWAYS use "Contributed" — this was research team work, not sole development.
+
+---
+
+### FC-3: MISSION — Maritime Microservice Platform
+**Significance:** Hands-on microservices + Docker in 2018–2019 — predates the containerization wave. Shows early adoption of modern architecture patterns.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | MED | Built | Early Docker/microservice signal — pair with FC-1 |
+| Analytics Engineer | LOW | — | Omit |
+| ML/AI Engineer | LOW | — | Omit |
+| Data Platform/Infra | MED | Built | Early containerization signal |
+
+---
+
+### FC-4: Predictive Maintenance Grant Contribution
+**Significance:** Minimal — contributed to a grant proposal. Include only in CL for research-adjacent roles.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| All roles | LOW | Contributed | CL mention only — not a resume bullet |
+
+---
+
+## VIZRT ACHIEVEMENTS
+
+---
+
+### VZ-1: Distributed Video Transcoding Backend
+**Significance:** Python + C++ in a distributed backend for a globally-deployed broadcast platform (CNN, BBC, Al Jazeera scale). Confirms systems programming capability and international team experience.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | MED | Engineered | Include as backend systems depth signal |
+| Analytics Engineer | LOW | — | Omit |
+| ML/AI Engineer | LOW | — | Omit |
+| Data Platform/Infra | MED | Engineered | Include for distributed systems signal |
+
+---
+
+### VZ-2: Test Automation + CI/CD Quality Gates Integration
+**Significance:** Owning the quality gate mechanism in a CI/CD pipeline for production broadcast software — more than just test writing. Shortening feedback loop and time-to-market at a company serving global broadcasters.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | MED | Built | Include as CI/CD quality ownership signal |
+| Analytics Engineer | LOW | — | Omit |
+| ML/AI Engineer | LOW | — | Omit |
+| Data Platform/Infra | MED | Built | Include for CI/CD depth; quality gates framing |
+
+**Note:** For tight 2-page budgets, combine VZ-1 and VZ-2 into a single 2L bullet for Vizrt position.
+
+---
+
+## GENERALI ACHIEVEMENTS
+
+---
+
+### GN-1: BDD Technical Ownership & Team Evangelism
+**Significance:** Introduced a practice (BDD) to an organization and then held technical ownership of it — demonstrates initiative, technical leadership, and knowledge-transfer capability. Strongest signal from Generali period.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| Staff/Senior Data Engineer | MED | Introduced | Include as initiative + technical leadership thread from earlier career |
+| Analytics Engineer | LOW | — | Omit |
+| ML/AI Engineer | LOW | — | Omit |
+| Data Platform/Infra | LOW | — | Omit |
+
+---
+
+### GN-2: UIPath RPA POC
+**Significance:** Early RPA experience — niche signal. Only relevant for roles explicitly targeting automation engineering.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| All roles | LOW | Developed | Omit from resume; include in CV if space |
+
+---
+
+### GN-3 & GN-4: Java/J2EE Development + IBM ODM
+**Significance:** Early-career Java and enterprise software context. Not differentiating at current career stage.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| All roles | LOW | — | CV only — early career context |
+
+---
+
+## CAPGEMINI ACHIEVEMENTS
+
+---
+
+### CA-1: GUI Test Automation — Transport Logistics Client
+**Significance:** Establishes the test automation thread from day one of career. Zeugnis rates "vollsten Zufriedenheit" (top tier) despite being only 6 months. Historical context only at current career stage.
+
+| Role Type | Priority | Lead Verb | Framing Angle |
+|-----------|----------|-----------|---------------|
+| All roles | LOW | Implemented | CV only — do not include on 2-page resume |
+
+---
+
+## Master Priority Matrix (Cross-Role)
+
+| Achievement | Staff/Senior DE | Analytics Eng | ML/AI Eng | Data Platform/Infra |
+|-------------|----------------|---------------|-----------|---------------------|
+| SW-1 AWS Migration | HIGH | HIGH | MED | HIGH |
+| SW-2 Component Owner | HIGH | HIGH | MED | HIGH |
+| SW-3 K8s + GitLab | HIGH | MED | HIGH | HIGH |
+| SW-4 B2B Products | MED | HIGH | LOW | LOW |
+| SW-5 Security Champion | MED | LOW | MED | HIGH |
+| SW-6 PySpark | MED | LOW | MED | MED |
+| BS-1 ML Inference | HIGH | LOW | HIGH | HIGH |
+| BS-2 Data Services | HIGH | MED | MED | HIGH |
+| BS-3 App Owner | HIGH | HIGH | MED | HIGH |
+| BS-4 ELK PoC | MED | LOW | MED | HIGH |
+| FC-1 SCEDAS + CI/CD | MED | LOW | LOW | MED |
+| FC-2 ARTUS ML/NLP | MED | LOW | HIGH | LOW |
+| FC-3 MISSION Microsvcs | MED | LOW | LOW | MED |
+| VZ-1 Video Backend | MED | LOW | LOW | MED |
+| VZ-2 CI/CD Quality Gates | MED | LOW | LOW | MED |
+| GN-1 BDD Ownership | MED | LOW | LOW | LOW |
+| GN-2 UIPath RPA | LOW | LOW | LOW | LOW |

resume_builder/support/pub_metadata.md

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+# Publication Metadata — Dennis Thiessen
+
+> Generated: 2026-03-28
+
+---
+
+## Summary
+
+- **Academic publications:** 0
+- **Peer-reviewed papers:** 0
+- **Conference papers:** 0
+- **Research projects (named, employer-confirmed):** 3 (ARTUS, MISSION, Predictive Maintenance grant)
+- **Personal projects:** 1 (RiskAhead — discontinued)
+
+Dennis is a software and data engineering professional, not an academic researcher. His Fraunhofer CML period involved applied research, but as a software contributor — not a publishing author. Do not include a "Publications" section on resume or CV.
+
+---
+
+## Named Research Projects (for CV / CL context only)
+
+| # | Project | Institution | Period | Dennis's Role | Output |
+|---|---------|-------------|--------|--------------|--------|
+| 1 | ARTUS | Fraunhofer CML | 2018–2019 | Contributing developer (ML/NLP components) | Internal research / prototype |
+| 2 | MISSION | Fraunhofer CML | 2018–2019 | Developer (microservice layer) | Internal research platform |
+| 3 | Predictive Maintenance Grant | Fraunhofer CML | 2018–2019 | Contributor ("Mitarbeit") | Grant proposal (outcome unknown) |
+
+**Framing rule:** These are research *projects*, not publications. List them under the Fraunhofer experience entry (not a Publications section). Use "Research project" framing. Do NOT imply peer-reviewed output.
+
+---
+
+## Personal Projects
+
+| # | Project | Period | Status | Notes |
+|---|---------|--------|--------|-------|
+| 1 | RiskAhead | 2015–2017 | Discontinued | Android app (Java, PHP, MySQL, Docker) — incident/hazard mapping. Featured in VICE Germany. Personal project only — not peer-reviewed, not commercial. |
+
+**Framing rule:** If included, list under Projects section with explicit "Personal project" label. Media mention (VICE Germany) can be noted as: "Featured in VICE (Germany)". Do NOT list VICE as a publication credit.
+
+---
+
+## Master's Thesis (academic output)
+
+| Field | Value |
+|-------|-------|
+| Title | "Development of a Web-Based Remote Fault Diagnosis System" |
+| Institution | Tongji University, Shanghai (exchange thesis) + Universität der Bundeswehr München |
+| Year | 2013 |
+| Grade | 1.0 (Very Good — top German grade) |
+| Methods | Neural Networks, Particle Swarm Optimization, Fuzzy Networks |
+| Status | Completed academic thesis — not published as a paper |
+
+**Framing rule:** List under Education section only. Grade 1.0 may be highlighted for roles where academic performance is valued (rare in industry). Methods can be mentioned in CL for ML/AI roles to show early exposure.
+
+---
+
+## Certifications as Evidence (not publications)
+
+See `skills_taxonomy.md` Category 10 for full cert list. Certs replace publications as credentialing signals for industry roles — list in Certifications section, not Publications.

resume_builder/support/significance_bosch.md

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+# Significance Research: Bosch Semiconductor — Data Analysis Engineer
+
+> Use in cover letters and summaries — NOT in resume bullet text.
+> Particularly valuable for semiconductor industry JDs.
+
+---
+
+### BS-1: ML Inference in 24/7 Semiconductor Fab — Field Context
+
+**The problem:** Semiconductor manufacturing generates enormous volumes of image data (SEM, optical inspection, parametric test data) that traditionally required manual review by process engineers to identify defects. Manual inspection is slow, inconsistent, and a bottleneck as wafer volumes scale.
+
+**The industry direction:** Computer vision / image classification ML has been adopted by leading semiconductor manufacturers (Intel, TSMC, ASML, Infineon) to automate defect detection. The challenge is not building the model — it's deploying it reliably into a 24/7 production environment where downtime is measured in wafer yield loss.
+
+**Competing approaches:**
+- Rule-based inspection systems (legacy — deterministic but limited to known defect patterns)
+- Offline ML analysis (batch — not real-time, misses process drifts)
+- Inline ML inference (real-time, containerized — current best practice)
+
+**Why Dennis's experience matters:** Deploying ML inference into a 24/7 fab is operationally much harder than deploying to a web server. There are no maintenance windows, hardware is constrained, and a model failure affects production throughput. Dennis designed and executed the integration strategy for this environment — a level of MLOps maturity that few data engineers have encountered.
+
+**Differentiation:** The combination of Docker containerization + Kubernetes orchestration + Ansible automation in a 24/7 constrained environment is a rare and credible production ML deployment signal.
+
+---
+
+### Semiconductor Data Domains — Field Context
+
+**Defect Management:**
+Semiconductor defect management involves tracking, classifying, and correlating defects found during inline inspection (optical, SEM) and end-of-line electrical test. Key data challenges: high-dimensional spatial data (wafer maps), multi-step process correlation, and connecting defect signatures to root causes (process excursions, equipment issues). Dennis built data pipelines and ML systems directly in this domain.
+
+**Semiconductor Parameter Testing:**
+Parametric testing measures electrical characteristics (threshold voltages, leakage currents, resistance) of test structures on each wafer. The data volume is massive — hundreds of parameters across thousands of dies per wafer, across thousands of wafers per day. Data engineering for parametric test requires efficient storage, fast query access, and statistical analysis capabilities. Dennis built data services that fed parametric testing analysis teams.
+
+**Process Analysis:**
+Process analysis correlates equipment parameters (temperature, pressure, gas flow) with downstream wafer yield and defect outcomes. This is the domain where data engineering meets process engineering — the pipelines must be reliable and the data must be accurate, because process decisions (equipment maintenance, recipe adjustments) depend on it.
+
+**Why this is rare:** Most data engineers have worked in SaaS, finance, or e-commerce. Semiconductor manufacturing data — with its specialized domain vocabulary, data types (wafer maps, SPC charts, lot genealogy), and operational constraints — is a niche that few candidates can credibly claim.
+
+---
+
+### Field Overview: Data & AI in Semiconductor Manufacturing (2024–2026)
+
+The semiconductor industry is undergoing a major digital transformation driven by:
+1. **Process complexity:** 300mm fabs with 1000+ process steps generate petabytes of data; manual analysis can no longer keep pace
+2. **Yield pressure:** At leading-edge nodes, even 1% yield improvement has enormous economic value — data-driven yield optimization is a strategic priority
+3. **AI/ML adoption:** Computer vision for inline inspection, predictive maintenance for equipment, and ML-based process optimization are all actively deployed at tier-1 fabs (TSMC, Intel, Samsung)
+4. **Talent scarcity:** Candidates who combine data engineering depth with semiconductor domain knowledge are extremely rare — most data engineers lack the domain; most process engineers lack the data skills
+
+**Target companies for semiconductor JDs:**
+ASML, Infineon, GlobalFoundries, ams OSRAM, Microchip Technology, ON Semiconductor, Renesas, NXP, STMicroelectronics, Bosch (again), TSMC (Europe fabs in Dresden area), Wolfspeed, SiCrystal, Elmos
+
+**CL hook for semiconductor JDs:**
+> "Semiconductor manufacturing analytics is one of the most data-intensive and operationally demanding domains in industry. At Bosch Semiconductor in Dresden, I worked directly in the data domains that matter most — Defect Management, Semiconductor Parameter Testing, and Process Analysis — building the pipelines and analytics platforms that engineers relied on for real-time production decisions. That domain knowledge, combined with my experience deploying ML-based defect classification into a 24/7 fab, is what I'd bring to [Company]."

resume_builder/support/significance_swisscom.md

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+# Significance Research: Swisscom — Staff Data, Analytics & AI Engineer
+
+> Use in cover letters and summaries — NOT in resume bullet text.
+> Provides field context demonstrating awareness of the data engineering landscape.
+
+---
+
+### SW-1: AWS Migration — Field Context
+
+**The problem:** Legacy enterprise data warehouses (Teradata, Oracle) are expensive to scale, inflexible for modern analytics workloads, and difficult to integrate with ML pipelines. The industry-wide shift to cloud-native data platforms (AWS, Azure, GCP) is driven by cost, elasticity, and the rise of the data lakehouse pattern.
+
+**Competing approaches:** Most enterprises face a choice between lift-and-shift (rehosting on cloud VMs — minimal benefit), re-platforming (moving to managed services like Redshift), or full re-architecture to a lakehouse (S3 + Athena/Iceberg + Glue). The lakehouse pattern (Apache Iceberg on S3 + Athena) is increasingly the de facto standard for cost-efficient, ACID-compliant analytics at scale.
+
+**Why this matters:** Swisscom serves millions of Swiss customers across mobile, broadband, and enterprise — the data volume is significant. Moving Fulfillment data pipelines to a cloud-native architecture directly affects the speed and cost of analytics for business-critical processes.
+
+**Differentiation:** Dennis didn't just configure existing pipelines in a new environment — he introduced Apache Iceberg (open table format with time-travel and schema evolution), AWS Glue Tables as the catalog, and CloudFormation for IaC provisioning. This reflects current best practices in data lakehouse architecture, not a basic ETL migration.
+
+**Field overview: Data Lakehouse Architecture (2024–2026)**
+The data lakehouse pattern — combining the scalability of data lakes (S3, ADLS) with the ACID guarantees and query performance of data warehouses — has become the dominant architecture for new data platform builds. Apache Iceberg has emerged as the leading open table format, supported by AWS (Athena, Glue), Databricks (as Delta Lake alternative), and Snowflake. Engineers who have implemented Iceberg in production (not just read about it) are in high demand as organizations migrate off proprietary DWH systems.
+
+---
+
+### SW-2: Component Ownership at Scale — Field Context
+
+**The problem:** In large data engineering teams at enterprise companies, the "Component Owner" model is how organizations assign accountability for production systems. Unlike a ticket-based dev model, Component Owners are responsible for a system's full lifecycle: reliability, compliance, SLA, on-call, and stakeholder communication. This is a Staff-engineer-level responsibility.
+
+**Why this matters:** Swisscom's Fulfillment domain carries business-critical data — provisioning, activating, and tracking customer service orders for Switzerland's largest telecom. Pipeline failures in this domain directly impact customer experience and revenue.
+
+**Differentiation:** Dennis holds this responsibility as a Staff Engineer (Engineer IV) — the same person building the pipelines is accountable for their reliability in production. This is the "full-stack data engineer" model that platform teams increasingly demand.
+
+---
+
+### SW-3: Kubernetes for Data Applications — Field Context
+
+**The problem:** Data pipelines have traditionally been deployed on bare metal or VMs, leading to environment inconsistency, difficult scaling, and slow deployments. The shift to Kubernetes for data workloads (not just web services) reflects the maturation of the data platform discipline.
+
+**Industry trend:** Running data applications (Airflow, Spark, custom Python pipelines) on Kubernetes is now standard practice at mature data organizations. GitLab CI/CD with Kubernetes deployment is the Swiss/European enterprise standard (as opposed to GitHub Actions + AWS ECS in US-heavy startups).
+
+**Differentiation:** Swisscom's use of Kubernetes for Python data applications confirms production-grade container orchestration for data workloads — not just a dev/test environment.
+
+---
+
+### Field Overview: Modern Data Engineering (2024–2026)
+
+The data engineering discipline has undergone a significant shift in the past 3 years:
+1. **From batch to streaming:** Kafka-based event-driven architectures have replaced many nightly batch processes
+2. **From proprietary DWH to open lakehouse:** Teradata/Oracle → S3 + Athena/Iceberg is the dominant migration pattern
+3. **From manual to automated infra:** CloudFormation, Terraform, and Pulumi have made IaC standard for data platform teams
+4. **From separated to embedded ML:** Data engineers who can own the ML data layer (not just supply data to a separate ML team) are increasingly valuable
+
+Dennis's current stack (Kafka, PySpark, AWS S3/Glue/Athena/Iceberg, Kubernetes, GitLab CI/CD, CloudFormation) maps precisely to this modern paradigm.

resume_builder/support/skills_taxonomy.md

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+# Skills Taxonomy — Dennis Thiessen
+
+> Generated: 2026-03-28
+> Sources: All 10 extractions + 6 experience files
+> Use this file when populating the Technical Skills section of resume/CV.
+
+---
+
+## Summary Stats
+
+- **Total unique skills:** 65+
+- **Proficiency levels:** Expert (daily use, owned systems) | Proficient (shipped work, comfortable teaching) | Familiar (used in project, not current)
+- **Certification-backed skills:** AWS (SAA cert + Udacity DataEng), Software Architecture (iSAQB), AI/ML (Udacity AI for Trading, IBM AI Engineering)
+
+---
+
+## Category 1: Programming Languages
+
+| Skill | Proficiency | Evidence | Resume Weight |
+|-------|-------------|----------|---------------|
+| Python | Expert | Swisscom (pipelines, apps), Bosch (data services), Fraunhofer (ML/NLP), Vizrt (backend + tests) | HIGH |
+| SQL (multi-dialect) | Expert | All positions — Oracle, Impala, Teradata, MS SQL, Postgres, MySQL | HIGH |
+| PySpark | Proficient | Swisscom Staff level (LinkedIn confirmed) | HIGH |
+| Java | Proficient | Fraunhofer (SCEDAS, MISSION), Bosch (data services), Generali (J2EE), Capgemini | MED |
+| C# | Proficient | Bosch (data services, Spotfire extensions), Fraunhofer (SCEDAS) | MED |
+| JavaScript / TypeScript | Proficient | Fraunhofer (MISSION, Express.js), CV skills list | MED |
+| C++ | Proficient | Vizrt (backend transcoding), Generali (CV) | LOW |
+| VBA | Familiar | Student assistant role (Bundeswehr Uni, 2013) — very minor | LOW |
+
+---
+
+## Category 2: Data Engineering & Pipelines
+
+| Skill | Proficiency | Evidence | Resume Weight |
+|-------|-------------|----------|---------------|
+| ETL/ELT design & operation | Expert | Swisscom (component owner), Bosch (data services) | HIGH |
+| Apache Kafka | Expert | Swisscom (ingestion pipelines), Bosch (ELK PoC) | HIGH |
+| Apache Airflow | Proficient | Swisscom (AWS migration stack) | HIGH |
+| SAP BODS | Proficient | Swisscom (legacy ETL) | MED |
+| Teradata DWH | Proficient | Swisscom (DWH architecture + operation) | MED |
+| Hadoop / ImpalaSQL | Proficient | Bosch (data services over Hadoop) | MED |
+| Data modeling | Proficient | Swisscom (data products), Bosch (pipeline design) | MED |
+| SQL performance tuning | Proficient | CV (explain plans, indexes, partitions) | MED |
+| Apache Spark / PySpark | Proficient | Swisscom (big data processing) | HIGH |
+| dbt | Not confirmed | Not in any extraction — do not claim | — |
+
+---
+
+## Category 3: Cloud & Infrastructure
+
+| Skill | Proficiency | Evidence | Resume Weight |
+|-------|-------------|----------|---------------|
+| AWS (overall) | Proficient | Swisscom (migration), AWS SAA cert (2024), Udacity DataEng cert (2026) | HIGH |
+| AWS S3 | Proficient | Swisscom AWS migration | HIGH |
+| AWS Glue | Proficient | Swisscom AWS migration | HIGH |
+| AWS Athena | Proficient | Swisscom AWS migration (with Apache Iceberg table format) | HIGH |
+| AWS Glue (Jobs + Tables) | Proficient | Swisscom — Glue jobs for ETL + Glue Data Catalog / Glue Tables | HIGH |
+| Apache Iceberg | Proficient | Swisscom — S3 + Athena with Iceberg table format (open table format, time-travel, schema evolution) | HIGH |
+| AWS Redshift | Proficient | Swisscom AWS migration | HIGH |
+| AWS Lambda | Proficient | Swisscom AWS migration | MED |
+| AWS Step Functions | Proficient | Swisscom AWS migration | MED |
+| AWS CloudFormation | Proficient | Swisscom — IaaS, infrastructure provisioning as code | HIGH |
+| Kubernetes (K8s) | Expert | Swisscom (Python app deployment), Bosch (ML inference orchestration) | HIGH |
+| Docker | Expert | Bosch (ML containerization, ELK PoC), Fraunhofer (MISSION), Swisscom | HIGH |
+| Ansible | Proficient | Bosch (ML orchestration) | MED |
+| GitLab CI/CD | Proficient | Swisscom (confirmed Zeugnis) | HIGH |
+| Jenkins | Proficient | Fraunhofer (independently set up), Generali (BDD build jobs) | MED |
+| CI/CD (general) | Expert | Swisscom, Fraunhofer, Vizrt, Generali — cross-position | HIGH |
+| IaC (Infrastructure as Code) | Proficient | Swisscom — AWS CloudFormation confirmed by user | HIGH |
+| DevSecOps | Proficient | Swisscom Security Champion ×3 (2023–2026), 100h training | MED |
+
+---
+
+## Category 4: Databases & Storage
+
+| Skill | Proficiency | Evidence | Resume Weight |
+|-------|-------------|----------|---------------|
+| Oracle DB | Expert | Swisscom (Fulfillment pipelines), Bosch (data services), Generali (web portal) | HIGH |
+| Teradata | Proficient | Swisscom (DWH target, architecture) | MED |
+| MS SQL Server | Proficient | Fraunhofer (SCEDAS — Entity Framework) | LOW |
+| PostgreSQL | Familiar | CV skills list | LOW |
+| MySQL | Familiar | CV skills list, RiskAhead project | LOW |
+| SQLite | Familiar | Fraunhofer (MISSION microservices) | LOW |
+| Hadoop / Impala | Proficient | Bosch (ImpalaSQL data services) | MED |
+
+---
+
+## Category 5: ML & AI
+
+| Skill | Proficiency | Evidence | Resume Weight |
+|-------|-------------|----------|---------------|
+| ML inference deployment | Proficient | Bosch (Docker/K8s in 24/7 fab — primary responsibility) | HIGH |
+| Image classification | Proficient | Bosch (automated quality monitoring in semiconductor fab) | MED |
+| NLP / Speech recognition | Familiar | Fraunhofer ARTUS research project (contributing role) | MED |
+| PyTorch | Familiar | CV skills list | LOW |
+| Scikit-learn | Familiar | CV skills list | LOW |
+| Pandas / NumPy | Proficient | CV (data analysis, pipeline work) | MED |
+| Matplotlib / Plotly | Proficient | CV (data visualization, dashboards) | LOW |
+| MLOps (general) | Proficient | Bosch (full ML lifecycle: containerize → deploy → monitor in production) | HIGH |
+| AI for Trading / Quant ML | Familiar | Udacity AI for Trading Nanodegree (2021) — personal study, not professional | LOW |
+| TensorFlow / Keras | Familiar | IBM AI Engineering Specialization (Coursera) | LOW |
+| Apache Spark ML | Familiar | IBM AI Engineering (Spark ML course) | LOW |
+
+**Proficiency note:** For ML/AI roles, frame Bosch ML deployment as primary evidence. NLP/ARTUS and the Udacity/IBM certs as supporting signals. Do not overstate ML modeling depth — the core strength is ML *infrastructure and deployment*, not research.
+
+---
+
+## Category 6: Testing & Quality Engineering
+
+| Skill | Proficiency | Evidence | Resume Weight |
+|-------|-------------|----------|---------------|
+| Test automation | Expert | Capgemini, Generali, Vizrt — consistent across 3 positions | MED (earlier career) |
+| BDD (Behaviour-Driven Development) | Proficient | Generali — introduced PoC, held technical ownership | MED |
+| Serenity-BDD / JBehave | Proficient | Generali (confirmed Zeugnis) | LOW |
+| Selenium | Proficient | Generali (UI test automation) | LOW |
+| pytest | Proficient | CV skills list | MED |
+| TDD | Proficient | Capgemini, Generali (confirmed) | LOW |
+| HP Quality Center / ALM | Familiar | Capgemini (Zeugnis confirmed) | LOW |
+| UIPath RPA | Familiar | Generali (POC developer, confirmed Zeugnis + LinkedIn) | LOW |
+| Camunda BPMN | Familiar | Generali (LinkedIn confirmed) | LOW |
+| Quality gates (CI/CD) | Proficient | Vizrt (CI/CD integration), Fraunhofer (Jenkins quality gates) | MED |
+
+---
+
+## Category 7: Observability, Monitoring & DevOps Tooling
+
+| Skill | Proficiency | Evidence | Resume Weight |
+|-------|-------------|----------|---------------|
+| ELK Stack (Elasticsearch/Logstash/Kibana) | Proficient | Bosch (anomaly detection PoC — primary developer) | MED |
+| Grafana | Proficient | Bosch (monitoring dashboards) | MED |
+| Prometheus | Proficient | Bosch (metrics) | MED |
+| Loki | Familiar | Bosch (log aggregation, part of PoC) | LOW |
+| Git | Expert | All positions | HIGH |
+| Agile / Scrum | Proficient | Swisscom (confirmed Zeugnis — backlog, sprint planning, Product Owner collaboration) | MED |
+| Tibco Spotfire | Familiar | Bosch (C# extensions, LinkedIn confirmed) | LOW |
+
+---
+
+## Category 8: Frameworks & APIs
+
+| Skill | Proficiency | Evidence | Resume Weight |
+|-------|-------------|----------|---------------|
+| Flask / FastAPI / Django | Proficient | CV skills list | MED |
+| Express.js | Familiar | Fraunhofer MISSION (microservices) | LOW |
+| Entity Framework (.NET) | Proficient | Fraunhofer SCEDAS | LOW |
+| Spring Boot | Familiar | Generali (Dispatcher PoC, Apache Camel) | LOW |
+| Apache Camel | Familiar | Generali (Dispatcher PoC) | LOW |
+| SQLAlchemy | Familiar | CV skills list | LOW |
+| Swagger / OpenAPI | Familiar | CV skills list | LOW |
+
+---
+
+## Category 9: Domain Knowledge
+
+| Domain | Depth | Source | Resume Weight |
+|--------|-------|--------|---------------|
+| Telecom / Enterprise data platforms | Proficient | Swisscom (2+ years, current) | HIGH |
+| Semiconductor manufacturing / Industry 4.0 | Proficient | Bosch (3 years) — data domains: Defect Management, Semiconductor Parameter Testing, Process Analysis, Image-based Quality Inspection | MED |
+| Maritime logistics | Familiar | Fraunhofer CML (1 year research) | LOW |
+| Broadcast technology | Familiar | Vizrt (1 year) | LOW |
+| Insurance IT / Business process automation | Familiar | Generali (2 years) | LOW |
+| Security / DevSecOps | Proficient | Swisscom Security Champion ×3 | MED |
+| Blockchain / Web3 | Familiar | Personal — RPC APIs, basic Solidity, Kraken since 2017 | LOW (bonus only) |
+
+---
+
+## Category 10: Certifications (Skills Signals)
+
+| Certification | Issuer | Year | Active | Resume Weight |
+|--------------|--------|------|--------|---------------|
+| AWS Certified Solutions Architect – Associate | AWS | 2024 | Yes (until Sep 2027) | HIGH |
+| Data Engineering with AWS (Nanodegree) | Udacity | 2026 | Yes | HIGH |
+| iSAQB Certified Professional for Software Architecture — Foundation Level | iSAQB | 2016 | Yes (no expiry) | MED |
+| ITIL® Foundation Certificate in IT Service Management | PEOPLECERT / AXELOS | 2016 | Yes (no expiry) | LOW |
+| AI for Trading Nanodegree | Udacity / WorldQuant | 2021 | Yes | LOW (niche) |
+| Swisscom Security Champion | Swisscom (internal) | 2023–2026 | Active | MED (as bullet, not cert line) |
+| IBM AI Engineering Specialization | IBM / Coursera | — | Yes | LOW |
+
+---
+
+## Skills Config Guide (for resume generation)
+
+Refers to `config.md` skills layout: **4-3-2-2-2** (resume) or **4-4-3-3-3** (CV).
+
+### Suggested Resume Skills Groups (5 groups)
+
+| Group | Label | Skills to include |
+|-------|-------|------------------|
+| 1 (4 lines) | Languages & Data | Python, PySpark, SQL (Oracle · Impala · Teradata · Postgres), Java · C# |
+| 2 (3 lines) | Cloud & Infra | AWS (S3 · Glue · Athena · Redshift · Airflow), Kubernetes · Docker · Ansible, GitLab CI/CD · Jenkins |
+| 3 (2 lines) | Pipelines & Platforms | Kafka · Airflow · SAP BODS · Hadoop, Teradata DWH · ETL/ELT design |
+| 4 (2 lines) | ML & Observability | ML inference deployment · MLOps · PyTorch · Scikit-learn, ELK Stack · Grafana · Prometheus |
+| 5 (2 lines) | Certifications | AWS Certified Solutions Architect – Associate (active), iSAQB CPSA Foundation · ITIL v3 · Data Engineering with AWS (Udacity) |
+
+**Adjust per JD:** For ML/AI roles, swap group 4 to lead with ML; for Platform/Infra roles, expand cloud group. The cert line (group 5) is fixed per `config.md`.